Aggregate Light Estimation for XR Temporal Consistency
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Solution Overview
Problem
Current light estimation techniques in extended reality environments struggle to provide temporally and spatially consistent light estimation, leading to inaccuracies in blending virtual content with real-world content and inefficient image processing operations.
Innovation Solution
The system generates an aggregate light estimate by combining individual light estimates from multiple frames using a neural network with an encoder-decoder architecture and a long short-term memory (LSTM) unit, while also utilizing a 3D reconstruction to filter out obstructions and account for changes in light sources over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If current light estimation techniques are used in extended reality environments, then processing speed is maintained, but temporal and spatial consistency of light estimation deteriorates
Solution Approach 1:
The patent combines multiple individual light estimates from different frames into a single aggregate light estimate by merging their feature vectors. This consolidation approach improves temporal and spatial consistency while maintaining measurement precision through the collective information from multiple frames.
Solution Approach 2:
The system continuously processes multiple frames to generate individual light estimates and maintains a running aggregate estimate. This continuous processing ensures temporal consistency by incorporating information from the entire video sequence rather than isolated frames.
2Measurement precision
If multiple frames are processed individually for light estimation, then measurement precision improves, but processing time increases
Solution Approach 1:
The patent extracts light estimation feature vectors from multiple frames in advance and stores them for later aggregation. This preliminary processing allows the system to have individual frame estimates ready without performing the aggregation computation at the moment it is needed, thereby reducing real-time processing time.
Solution Approach 2:
By merging pre-computed feature vectors from multiple frames into a single aggregate estimate, the system achieves accurate light estimation without repeatedly processing all frames individually at runtime, thus reducing processing time while maintaining precision.
3Reliability
If virtual content is rendered without accurate light estimation, then rendering speed is maintained, but visual coherence with real-world content deteriorates
Solution Approach 1:
The patent uses an aggregate light estimate as an intermediary representation that captures the essential lighting characteristics of a scene from multiple frames. This intermediary provides sufficient information for coherent virtual content rendering without requiring full processing of all original frame data, thus maintaining both reliability and productivity.
4Speed
If individual light estimates from each frame are used, then responsiveness to light changes is improved, but temporal consistency deteriorates
Solution Approach 1:
The system uses the aggregate light estimate as a feedback reference to detect changes in lighting conditions. By comparing individual frame estimates against the aggregate, the system can identify actual light changes while filtering out noise, thus maintaining both responsiveness and temporal consistency.
Data Source
AI summary
Techniques and systems are provided for light estimation. In some examples, a system receives a plurality of frames associated with a scene. The plurality of frames includes a first frame and a second frame occurring after the first frame. The system determines, based on image data of the first frame, a first light estimate associated with the scene. The system also determines, based on image data of the second frame, a second light estimate associated with the scene. The system further generates an aggregate light estimate associated with the scene based on combining the second light estimate with at least the first light estimate.